# CPU-efficient inference

Quantized version of Qwen3-8B, utilizing IQ-DynamicGate ultra-low bit quantization technology to optimize memory efficiency and inference speed

GLM-Z1-9B-0414 is a bilingual text generation model supporting both Chinese and English, utilizing the GGUF format and suitable for various quantization levels, from BF16 to ultra-low-bit quantization (1-2 bits).

Ultra-low-bit quantization (1-2 bits) model using IQ-DynamicGate technology, suitable for memory-constrained devices and edge computing scenarios

An ultra-low bit quantized model optimized based on the Llama-3-8B architecture, utilizing IQ-DynamicGate technology for adaptive 1-2 bit precision quantization, suitable for memory-constrained environments.

An 8B parameter model based on the Llama-3 architecture, optimized for memory usage with IQ-DynamicGate ultra-low bit quantization technology

This is an instruction-tuned model based on Mistral-Small-3.1-24B-Base-2503, utilizing GGUF format and IQ-DynamicGate ultra-low bit quantization technology.

Llama-3.1-8B-Instruct is an instruction-tuned version based on Llama-3-8B, utilizing IQ-DynamicGate technology for ultra-low-bit quantization (1-2 bits), enhancing accuracy while maintaining memory efficiency.

Mistral-7B-Instruct-v0.2 is an instruction-tuned model based on the Mistral-7B architecture, supporting text generation tasks, optimized for memory efficiency using IQ-DynamicGate ultra-low bit quantization technology.

A text classification model built on fasttext, used to determine whether text meets textbook-level data quality, serving as a data filtering tool for large language model training.